Poster Number 19
See more from this Division: SubmissionsSee more from this Session: Graduate Student Poster – Soils
Sunday, February 3, 2013
Accurately estimating the amount of plant-available N from soil and crop residues is important for N management in cropping systems. The ability to estimate the amount of N from cover crop mulch that will become plant available helps to reduce over and under application of mineral fertilizers and helps smallholder farmers maximize the critical services that organic inputs provide in regards to agricultural productivity. Estimating the amount of N that will be mineralized from cover crop residues is challenging because of the complexity of the process and the variety of factors involved, including residue quality, temperature, water content, drying and rewetting events, and soil characteristics. Because many factors are involved in the mineralization process, simulation models are useful tools for estimating N mineralized from cover crops. Some of the most widely used models for simulating an entire crop-soil system are CERES models. Previous work in Georgia showed that the N subroutine of the DSSAT (Decision Support System for Agrotechnology Transfer) family of models, CERES-N, could be calibrated to provide accurate estimates of N released from rye (Secale cereal L.), wheat (Triticum aestivum L.), oats (Avena sativa L.), and crimson clover (Trifolium incarnatum L.) residues when decomposing on the surface of a Cecil soil. Rate constants in these models need to be validated for different cover crop residues and soils. The objective of this study is to provide data needed to validate the rate constants of mineralization for cover crop residues identified in previous studies. We will compare the CERES-N simulation results with N mineralization results from an in-situ field study. Crimson clover (Trifolium incarnatum L.) and rye (Secale cereale L.) will be allowed to decompose on the surface of the soil. Samples will be collected over 120 d; any error observed will be evaluated using statistical tools such as RMSE.
See more from this Division: SubmissionsSee more from this Session: Graduate Student Poster – Soils